Aerosols Chemistry Clouds and Radiation

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Frontiers in Climate and Earth System Modeling: Advancing the Science

May 20, 2013

Geophysical Fluid Dynamics Laboratory

Speakers: Yi Ming and Chris Golaz

Moderator: Leo Donner

Using Models and Observations to

Understand

Climate Processes: Aerosols,

Chemistry, Clouds and Radiation



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•

Key to reducing model biases and uncertainties; Affirmed by the 2012 National Research Council

(NRC) Report on Advancing Climate Modeling and the

2010 NOAA Next-generation Strategic Plan (NGSP).

•

Relevant to NOAA’s climate adaptation andmitigation goal;

• Striving for a healthy balance between resolution and

complexity;

•

New opportunities created by “marrying” more

advanced physics and chemistry with finer spatial

resolutions.



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Overarching theme: Understanding the complexroles of short-lived species and clouds in

influencing climate and air quality.

1. Atmospheric Composition

• High resolution modeling of aerosol emissions and transport

[Paul Ginoux];

• Influence of inter-continental transport and stratospheric

intrusion on the western U.S. air quality [Meiyun Lin]



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2. Radiation and climate forcing• Parameterization of water vapor continuum [David Paynter];

• Validation of modeled surface radiative flux [Stuart

Freidenreich];

•

Surface radiative flux trends (global dimming) [Geeta Persad];• Active participation in the Atmospheric Chemistry and Climate

Model Intercomparsion Project (ACCMIP) [Vaishali Naik and

Larry Horowitz];

3. Climate response• Aerosol effects on South Asian monsoon [Massimo Bollasina];

• Non-local aerosol effects on the Atlantic Meridional Overturning

Circulation (AMOC) [Dan Schwarzkopf];



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4. Cloud Processes• Large-scale clouds and aerosol-cloud interactions [Chris

Golaz];

• Deep cumulus and satellite/process-level observations [Leo

Donner];

• Shallow cumulus and climate sensitivity [Ming Zhao].

Outline of this talk

1. Yi Ming: overview, and the first three research fields(atmospheric composition, radiation and climate response);

2. Chris Golaz: cloud processes, and future research directions.



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Accidents US I-60 near

Tulsa (Oct 19, 2012)

Credit: P. Ginoux

Dust Optical Depth and emission from

agriculture (Oct 18, 2012)

Nudged 50-km AM3 with land use dust sources (Ginoux et al., 2012)



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Sunphotometer @ CRT

Emission (mg/m2/day) by land use type

A O D ( 5 5 0

n m

)

date

ObsModel

0

20

40

60



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Southern California (May 23)

GFDL AM3

1 2 0

Lidar (CalNex)

O 3 S

M o

d e l 8 - h r S u r f a c e

O 3

( p p b )

Observed 8-hr Surface O3 (ppb)

Air qualitystandard

Stratospheric O3

(~50%)

A l t i t u d e ( k m

a . s . l .

)

Flight Track

O3 [ppb]

Lin et al. (2012) Credit: M. Lin

Apr. 12-16

Total O3 (100%)



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Credit: G. Persad

Global Energy

Balance Archive

(GEBA) stations

used in Norris

and Wild (2009)

The GFDL AM3/CM3 model has the best representation of the

dimming trends among all CMIP5 model (Allen et al., 2012).

Obs.

AM2.1

AM3



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C l e a

r - s k y s u r f a c e

s o

l a r r a

d i a t i o n

( W / m 2 ) Surface:

-12 W/m2

AM3

Ext.

Int. Mixing More Less Aerosol

+More absorption

More dimming

Less absorption

Less dimming

AM2.1 AM3

A b s or p t i on ( W

/ m2 )

-10

-6

-2

2

6

10

Absorption:8 W/m2

TOA

Surface

Cooling

Atmosphere

Warming

Similartrends



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Linear trends ofaverage JJAS rainfall

over central-northern

Indian (mm day-1)GG

All forcing

CRUAERO

Credit: M. Bollasina



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Climatology GG

All forcingAERO

ALL_FAERO

WMGGO3Climatology



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CRU

Obs.

May June

•

Aerosols tend to

increase rainfall

in May and June,

whilesuppressing it in

JAS;

•

An earlier

monsoon onset;

•

Consistent withobservations.

JAS (July-Aug-Sep)

AERO

Linear trends of precipitation [mm day-1 50 yr -1]



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warm &moist air

warm &moist air

May

June

Aerosols

Higher

SLP

LowerSLP

Surface

wind

Precipitation



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Sensitivity and forcing are key

climate properties. At

equilibrium:

!" " # $

$ "#$%&'()$* forcing: radiative"#$%&'()$* forcing: radiative

perturbation (GHGs, aerosols,clouds, land-use,!)

# sensitivity: temperaturesensitivity: temperature

response per unit of forcing.

! Clouds impact both.

Forster et al. (2013); Kiehl (2007).

Anti-correlation between sensitivity

and forcing among models that

reproduce observed warming.



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Zhao (2013 , J. Climate, submitted)

Red: tropical average

Blue: global average

Credit: M. Zhao

"

Increased climate sensitivityin HiRAM (and AM3)

compared to AM2.

" Sensitivity highly correlated

with cloud feedback (change

in cloud radiative effect).

Cloud feedback

! impacted by details of

convective parameterization,

! linked to convective

precipitation efficiency.

S e n

s i t i v i t y

Cloud feedback parameter AM2

0 c48 HiRAM

1-4 perturbed cumulus mixing

5-8 perturbed cumulus microphysics



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ModelsCM3: official GFDL CMIP5

model.

CM3w,c: configurations

with alternate but plausible

parameter choices.

NOAA NCDC 0.59 ºC

NASA GISS 0.53 ºC

HadCRUT3 0.56 ºC

CM3w 0.57 ºC

CM3 0.22 ºC

CM3c -0.01 ºC

Net warming

Golaz et al. (2013, GRL)Credit: C. Golaz

Observations

Models



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Evaluation of CFMIP1 and CFMIP2 modelsKlein et al. (2013, JGR)

g2: AM2(GAMDT, 2004,

J. Climate)

G3: AM3(Donner et al.,

2011, J. Climate)

AM3/CM3 results are being widely analyzed (53 citations to date).GFDL CFMIP credit: L. Donner, C. Seman, L. Horowitz, B. Hurlin

Shortwave relevant cloud properties Longwave relevant cloud properties



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• Long-standing stratocumulus biases

are reduced in AM3-CLUBB.

• Overall performance slightly lags AM3.

Credit: H. Guo

CLUBB* (NOAA/NSF Climate Process Team)Short-wave cloud forcing error [W m-2]

S t r a t o c u m u l u s b i a s e s

*Cloud Layers Unified by Bi-Normals



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! Full liquid water path response to aerosols could

potentially decrease magnitude of indirect effect.

Droplet concentration (cm-3)

CLUBB

Guo et al. (GRL, 2011)

L i q u i d w a t e r p a t h

( g m -

2 )

Credit: H. Guo

CLUBB* (NOAA/NSF Climate Process Team)Indirect effect for different stratocumulus cases

Droplet concentration (cm-3) L i q u i d w a t e r p a t h

( g m -

2 )

Ackerman et al. (Nature, 2004)

Large eddy simulations

*Cloud Layers Unified by Bi-Normals



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Improved understanding and new modeling capabilities:• Aerosol-ice cloud interactions (e.g., black carbon as ice nuclei);

• Double-moment aerosol/cloud microphysics;

• Aerosols (e.g., black carbon and dust) on snow;

•

Aerosol-vegetation-biogeochemistry coupling (e.g., dust andwild fires);

• Chemistry-climate interactions (e.g., methane lifetime);

• Improvement of radiative transfer parameterization (e.g., water

continuum);

• More unified and physically sound cloud and convective

parameterizations (e.g., CLUBB coupled with double-moment

cloud microphysics).



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Our process-oriented research generates1) mechanistic understanding of existing

model biases and uncertainties,

2) new modeling capabilities that enhance the

realism of regional climate and Earth

System simulation.

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Both aspects are crucial for developing the

next-generation GFDL Earth System Model, anessential tool for advancing NOAA’s climate

adaptation and mitigation goal.

Aerosols Chemistry Clouds and Radiation

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